R Tutorial
An introduction to R
Introduction
This tutorial is will introduce the reader to
,
a free, open-source statistical computing environment often used with
RStudio, a integrated development environment for
.
R Project Logo
Download
Download at https://www.r-project.org/
Download RStudio at https://rstudio.com/products/rstudio/download/
Calculator
can be used as a super awesome calculator
# 5 + 3 = 8
5 + 3 ## [1] 8
# 24 / (1 + 2) = 8
24 / (1 + 2) ## [1] 8
# 2 * 2 * 2 = 8
2^3 ## [1] 8
# 8 * 8 = 64
sqrt(64) ## [1] 8
# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000) ## [1] 8
Functions
has many useful built in functions
1:10## [1] 1 2 3 4 5 6 7 8 9 10
as.character(1:10)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
rep(1:2, times = 5)## [1] 1 2 1 2 1 2 1 2 1 2
rep(1:5, times = 2)## [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2)## [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, length.out = 7)## [1] 1 2 3 4 5 1 2
seq(5, 50, by = 5)## [1] 5 10 15 20 25 30 35 40 45 50
seq(5, 50, length.out = 5)## [1] 5.00 16.25 27.50 38.75 50.00
paste(1:10, 20:30, sep = "-")## [1] "1-20" "2-21" "3-22" "4-23" "5-24" "6-25" "7-26" "8-27" "9-28"
## [10] "10-29" "1-30"
paste(1:10, collapse = "-")## [1] "1-2-3-4-5-6-7-8-9-10"
paste0("x", 1:10)## [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9" "x10"
min(1:10)## [1] 1
max(1:10)## [1] 10
range(1:10)## [1] 1 10
mean(1:10)## [1] 5.5
sd(1:10)## [1] 3.02765
Custom Functions
Users can also create their own functions
customFunction1 <- function(x, y) {
z <- 100 * x / (x + y)
paste(z, "%")
}
customFunction1(x = 10, y = 90)## [1] "10 %"
customFunction2 <- function(x) {
mymin <- mean(x - sd(x))
mymax <- mean(x) + sd(x)
print(paste("Min =", mymin))
print(paste("Max =", mymax))
}
customFunction2(x = 1:10)## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"
for loops and if else
statements
xx <- NULL #creates and empty object
for(i in 1:10) {
xx[i] <- i*3
}
xx## [1] 3 6 9 12 15 18 21 24 27 30
xx %% 2 #gives the remainder when divided by 2## [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
if((xx[i] %% 2) == 0) {
print(paste(xx[i],"is Even"))
} else {
print(paste(xx[i],"is Odd"))
}
}## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
# or
ifelse(xx %% 2 == 0, "Even", "Odd")## [1] "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even"
paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))## [1] "3 is Odd" "6 is Even" "9 is Odd" "12 is Even" "15 is Odd"
## [6] "18 is Even" "21 is Odd" "24 is Even" "27 is Odd" "30 is Even"
Objects
Information can be stored in user defined objects, in multiple forms:
c(): a string of valuesmatrix(): a two dimensional matrix in one formatdata.frame(): a two dimensional matrix where each column can be a different formatlist():
A string…
xc <- 1:10
xc## [1] 1 2 3 4 5 6 7 8 9 10
xc <- c(1,2,3,4,5,6,7,8,9,10)
xc## [1] 1 2 3 4 5 6 7 8 9 10
A matrix…
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
A data frame…
xd <- data.frame(
x1 = c("aa","bb","cc","dd","ee",
"ff","gg","hh","ii","jj"),
x2 = 1:10,
x3 = c(1,1,1,1,1,2,2,2,3,3),
x4 = rep(c(1,2), times = 5),
x5 = rep(1:5, times = 2),
x6 = rep(1:5, each = 2),
x7 = seq(5, 50, by = 5),
x8 = log10(1:10),
x9 = (1:10)^3,
x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
A list…
xl <- list(xc, xm, xd)
xl[[1]]## [1] 1 2 3 4 5 6 7 8 9 10
xl[[2]]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
xl[[3]]## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
Selecting Data
xc[5] # 5th element in xc## [1] 5
xd$x3[5] # 5th element in col "x3"## [1] 1
xd[5,"x3"] # row 5, col "x3"## [1] 1
xd$x3 # all of col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[,"x3"] # all rows, col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[3,] # row 3, all cols## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"## x4 x5
## 2 2 2
## 4 2 4
xl[[3]]$x1 # 3rd object in the list, col "x1## [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"
regexpr
xx <- data.frame(Name = c("Item 1 (detail 1)",
"Item 20 (detail 20)",
"Item 300 (detail 300)"),
Item = NA,
Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx## Name Item Detail
## 1 Item 1 (detail 1) Item 1 detail 1
## 2 Item 20 (detail 20) Item 20 detail 20
## 3 Item 300 (detail 300) Item 300 detail 300
Data Formats
Data can also be saved in many formats:
- numeric
- integer
- character
- factor
- logical
xd$x3 <- as.character(xd$x3)
xd$x3## [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
xd$x3 <- as.numeric(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
xd$x3 <- as.factor(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
xd$x10## [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
as.numeric(xd$x10) # TRUE = 1, FALSE = 0## [1] 1 1 1 0 0 1 1 0 0 0
sum(xd$x10)## [1] 5
Internal structure of an object can be checked with
str()
str(xc) # c()## num [1:10] 1 2 3 4 5 6 7 8 9 10
str(xm) # matrix()## int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
str(xd) # data.frame()## 'data.frame': 10 obs. of 10 variables:
## $ x1 : chr "aa" "bb" "cc" "dd" ...
## $ x2 : int 1 2 3 4 5 6 7 8 9 10
## $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
## $ x4 : num 1 2 1 2 1 2 1 2 1 2
## $ x5 : int 1 2 3 4 5 1 2 3 4 5
## $ x6 : int 1 1 2 2 3 3 4 4 5 5
## $ x7 : num 5 10 15 20 25 30 35 40 45 50
## $ x8 : num 0 0.301 0.477 0.602 0.699 ...
## $ x9 : num 1 8 27 64 125 216 343 512 729 1000
## $ x10: logi TRUE TRUE TRUE FALSE FALSE TRUE ...
str(xl) # list()## List of 3
## $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## $ :'data.frame': 10 obs. of 10 variables:
## ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
## ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
## ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
## ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
## ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
## ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
## ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
## ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
## ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...
Packages
Additional libraries can be installed and loaded for use.
install.packages("scales")library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx## Values Rescaled
## 1 1 1.000000
## 2 2 4.222222
## 3 3 7.444444
## 4 4 10.666667
## 5 5 13.888889
## 6 6 17.111111
## 7 7 20.333333
## 8 8 23.555556
## 9 9 26.777778
## 10 10 30.000000
libraries can also be used without having to load them
scales::rescale(1:10, to = c(1,30))## [1] 1.000000 4.222222 7.444444 10.666667 13.888889 17.111111 20.333333
## [8] 23.555556 26.777778 30.000000
Data Wrangling
R for Data Science - https://r4ds.had.co.nz/
xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
Data1 = 1:10,
Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 X 6 60 66 6000
## 7 X 7 70 77 7000
## 8 X 8 80 88 8000
## 9 Y 9 90 99 9000
## 10 Y 10 100 110 10000
xx$Data1 < 5 # which are less than 5## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
xx[xx$Data1 < 5,]## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx[xx$Group == "X", c("Group","Data2","NewData1")]## Group Data2 NewData1
## 1 X 10 11
## 2 X 20 22
## 6 X 60 66
## 7 X 70 77
## 8 X 80 88
Data wrangling with tidyverse and pipes
(%>%)
library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
mutate(Data1 = 1:10,
Data2 = seq(10, 100, by = 10),
NewData1 = Data1 + Data2,
NewData2 = Data1 * 1000)
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 Y 6 60 66 6000
## 7 Y 7 70 77 7000
## 8 X 8 80 88 8000
## 9 X 9 90 99 9000
## 10 X 10 100 110 10000
filter(xx, Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Group == "X") %>%
select(Group, NewColName=Data2, NewData1)## Group NewColName NewData1
## 1 X 10 11
## 2 X 20 22
## 3 X 80 88
## 4 X 90 99
## 5 X 100 110
xs <- xx %>%
group_by(Group) %>%
summarise(Data2_mean = mean(Data2),
Data2_sd = sd(Data2),
NewData2_mean = mean(NewData2),
NewData2_sd = sd(NewData2))
xs## # A tibble: 2 × 5
## Group Data2_mean Data2_sd NewData2_mean NewData2_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X 60 41.8 6000 4183.
## 2 Y 50 15.8 5000 1581.
xx %>% left_join(xs, by = "Group")## Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean
## 1 X 1 10 11 1000 60 41.83300 6000
## 2 X 2 20 22 2000 60 41.83300 6000
## 3 Y 3 30 33 3000 50 15.81139 5000
## 4 Y 4 40 44 4000 50 15.81139 5000
## 5 Y 5 50 55 5000 50 15.81139 5000
## 6 Y 6 60 66 6000 50 15.81139 5000
## 7 Y 7 70 77 7000 50 15.81139 5000
## 8 X 8 80 88 8000 60 41.83300 6000
## 9 X 9 90 99 9000 60 41.83300 6000
## 10 X 10 100 110 10000 60 41.83300 6000
## NewData2_sd
## 1 4183.300
## 2 4183.300
## 3 1581.139
## 4 1581.139
## 5 1581.139
## 6 1581.139
## 7 1581.139
## 8 4183.300
## 9 4183.300
## 10 4183.300
Read/Write data
xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)For excel sheets, the package readxl can be used to read
in sheets of data.
library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")Tidy Data
Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html
yy <- xx %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = round(mean(DTF),1)) %>%
arrange(Location)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name Location Mean_DTF
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
## <chr> <dbl> <dbl> <dbl>
## 1 CDC Maxim AGL 86.7 134. 52.5
## 2 ILL 618 AGL 79.3 138. 47
## 3 Laird AGL 76.8 137. 56.8
yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name TraitName Value
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Name, value = Value)
yy## # A tibble: 3 × 4
## TraitName `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
## <chr> <dbl> <dbl> <dbl>
## 1 Jessore, Bangladesh 86.7 79.3 76.8
## 2 Metaponto, Italy 134. 138. 137.
## 3 Saskatoon, Canada 52.5 47 56.8
Base Plotting
We will start with some basic plotting using the base function
plot()
Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs
Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html
# A basic scatter plot
plot(x = xd$x8, y = xd$x9)# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)# Adjust plot type
plot(x = xd$x8, y = xd$x9, type = "line")# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")Now lets create some random and normally distributed data to make some more complicated plots
# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru## [1] 95.036607 64.299118 44.277868 62.737560 41.110003 31.448072 84.386636
## [8] 29.563884 84.818221 83.204736 47.356315 58.454673 37.291291 78.270141
## [15] 35.513189 44.921314 71.779533 9.237473 52.950839 39.745190 32.208791
## [22] 64.062901 34.251050 90.165302 22.055541 85.228169 58.085806 70.628836
## [29] 55.001248 37.157214 73.722328 56.957326 39.880074 65.031892 80.885361
## [36] 19.537287 13.288620 57.702788 58.341425 77.802338 90.486954 28.394933
## [43] 65.858972 97.649317 87.499397 15.585749 29.588063 1.292814 53.045638
## [50] 20.904310 65.303903 18.025759 33.514009 45.541695 58.166690 52.822755
## [57] 52.222039 27.661161 79.804155 66.311489 92.037330 24.262070 52.017771
## [64] 8.681582 6.674803 9.822665 98.696335 98.873699 13.410410 88.976303
## [71] 46.009659 55.357774 76.817716 59.007415 75.376665 14.096448 39.017125
## [78] 0.915982 30.806279 45.462567 54.601385 97.092707 24.327555 50.910370
## [85] 7.581679 26.108644 97.840213 21.570156 57.702659 93.898267 63.599669
## [92] 9.224599 90.473965 63.009056 4.746008 31.215992 99.739064 6.141104
## [99] 63.585246 6.050285
plot(x = ru)order(ru)## [1] 78 48 95 100 98 65 85 64 92 18 66 37 69 76 46 52 36 50
## [19] 88 25 62 83 86 58 42 8 47 79 96 6 21 53 23 15 30 13
## [37] 77 20 33 5 3 16 80 54 71 11 84 63 57 56 19 49 81 29
## [55] 72 32 89 38 27 55 39 12 74 4 94 99 91 22 2 34 51 43
## [73] 60 28 17 31 75 73 40 14 59 35 10 7 9 26 45 70 24 93
## [91] 41 61 90 1 82 44 87 67 68 97
ru<- ru[order(ru)]
ru## [1] 0.915982 1.292814 4.746008 6.050285 6.141104 6.674803 7.581679
## [8] 8.681582 9.224599 9.237473 9.822665 13.288620 13.410410 14.096448
## [15] 15.585749 18.025759 19.537287 20.904310 21.570156 22.055541 24.262070
## [22] 24.327555 26.108644 27.661161 28.394933 29.563884 29.588063 30.806279
## [29] 31.215992 31.448072 32.208791 33.514009 34.251050 35.513189 37.157214
## [36] 37.291291 39.017125 39.745190 39.880074 41.110003 44.277868 44.921314
## [43] 45.462567 45.541695 46.009659 47.356315 50.910370 52.017771 52.222039
## [50] 52.822755 52.950839 53.045638 54.601385 55.001248 55.357774 56.957326
## [57] 57.702659 57.702788 58.085806 58.166690 58.341425 58.454673 59.007415
## [64] 62.737560 63.009056 63.585246 63.599669 64.062901 64.299118 65.031892
## [71] 65.303903 65.858972 66.311489 70.628836 71.779533 73.722328 75.376665
## [78] 76.817716 77.802338 78.270141 79.804155 80.885361 83.204736 84.386636
## [85] 84.818221 85.228169 87.499397 88.976303 90.165302 90.473965 90.486954
## [92] 92.037330 93.898267 95.036607 97.092707 97.649317 97.840213 98.696335
## [99] 98.873699 99.739064
plot(x = ru)# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd## [1] 57.31938 52.83109 67.77015 55.18748 33.63995 54.90379 55.59814 62.48376
## [9] 46.42905 54.65412 51.87367 63.21216 57.70273 53.89610 31.53319 34.99689
## [17] 53.31073 56.46837 58.50772 45.79176 51.58524 59.41582 54.44566 43.63581
## [25] 50.69165 45.29070 55.49532 60.66887 55.53604 40.84987 45.93913 46.93736
## [33] 28.69660 65.55448 42.07341 51.12562 36.91262 53.01247 52.62362 62.62128
## [41] 62.19130 50.82076 67.29455 50.28424 51.99408 39.42766 61.45700 54.88958
## [49] 53.13180 53.52173 38.68911 57.25874 59.91703 32.96970 29.91831 34.93877
## [57] 43.59771 56.52579 45.92074 44.65374 41.57382 46.42467 35.66953 34.35970
## [65] 56.21520 58.23785 56.05175 48.81311 35.45030 57.01178 51.67037 71.43609
## [73] 21.63296 48.24366 63.08056 37.68397 52.45138 58.19703 53.26689 55.31310
## [81] 49.51008 37.82487 46.62249 53.27887 43.95725 58.59051 57.38111 45.36513
## [89] 48.31047 60.47889 46.80094 60.22038 50.97268 57.05727 47.41274 65.98625
## [97] 63.35533 58.56883 43.80609 55.92016
nd <- nd[order(nd)]
nd## [1] 21.63296 28.69660 29.91831 31.53319 32.96970 33.63995 34.35970 34.93877
## [9] 34.99689 35.45030 35.66953 36.91262 37.68397 37.82487 38.68911 39.42766
## [17] 40.84987 41.57382 42.07341 43.59771 43.63581 43.80609 43.95725 44.65374
## [25] 45.29070 45.36513 45.79176 45.92074 45.93913 46.42467 46.42905 46.62249
## [33] 46.80094 46.93736 47.41274 48.24366 48.31047 48.81311 49.51008 50.28424
## [41] 50.69165 50.82076 50.97268 51.12562 51.58524 51.67037 51.87367 51.99408
## [49] 52.45138 52.62362 52.83109 53.01247 53.13180 53.26689 53.27887 53.31073
## [57] 53.52173 53.89610 54.44566 54.65412 54.88958 54.90379 55.18748 55.31310
## [65] 55.49532 55.53604 55.59814 55.92016 56.05175 56.21520 56.46837 56.52579
## [73] 57.01178 57.05727 57.25874 57.31938 57.38111 57.70273 58.19703 58.23785
## [81] 58.50772 58.56883 58.59051 59.41582 59.91703 60.22038 60.47889 60.66887
## [89] 61.45700 62.19130 62.48376 62.62128 63.08056 63.21216 63.35533 65.55448
## [97] 65.98625 67.29455 67.77015 71.43609
plot(x = nd)hist(x = nd)hist(nd, breaks = 20, col = "darkgreen")plot(x = density(nd))boxplot(x = nd)boxplot(x = nd, horizontal = T)ggplot2
Lets be honest, the base plots are ugly! The ggplot2
package gives the user to create a better, more visually appealing
plots. Additional packages such as ggbeeswarm and
ggrepel also contain useful functions to add to the
functionality of ggplot2.
ggplot2 - https://ggplot2.tidyverse.org/
Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html
library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()mp + geom_point(aes(color = x3, shape = x3), size = 4)mp + geom_line(size = 2)mp + geom_line(aes(color = x3), size = 2)mp + geom_smooth(method = "loess")mp + geom_smooth(method = "lm")xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
rnorm(50, mean = 60, sd = 5)),
group = factor(rep(1:2, each = 50)),
label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")mp + geom_histogram(color = "black", position = "dodge")mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1mp + geom_density(alpha = 0.5)mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")mp + geom_boxplot() + geom_point()mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")library(ggbeeswarm)
mp + geom_quasirandom()mp + geom_quasirandom(aes(shape = group))mp2 <- mp + geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
common.legend = T, legend = "bottom")Statistics
Handbook of Biological Statistics - http://biostathandbook.com/
R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html
# Prep data
lev_Loc <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
mutate(Location = factor(Location, levels = lev_Loc),
Name = factor(Name, levels = lev_Name))
xx <- dd %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
## <fct> <dbl> <dbl> <dbl>
## 1 ILL 618 AGL 47 79.3 138.
## 2 CDC Maxim AGL 52.5 86.7 134.
## 3 Laird AGL 56.8 76.8 137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF,
color = Name, group = Name, shape = Name)) +
geom_point(size = 2.5, alpha = 0.7) +
geom_line(size = 1, alpha = 0.7) +
theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.
summary(aov(DTF ~ Name * Location, data = dd))## Df Sum Sq Mean Sq F value Pr(>F)
## Name 2 88 44 3.476 0.0395 *
## Location 2 65863 32931 2598.336 < 2e-16 ***
## Name:Location 4 560 140 11.044 2.52e-06 ***
## Residuals 45 570 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.
If we only have two things to compare, we could do a t-test.
xx <- dd %>%
filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)##
## Welch Two Sample t-test
##
## data: xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32.18265 -25.48402
## sample estimates:
## mean of x mean of y
## 52.11111 80.94444
DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.
xx <- dd %>%
filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
Location == "Metaponto, Italy") %>%
spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)##
## Welch Two Sample t-test
##
## data: xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059739 7.059739
## sample estimates:
## mean of x mean of y
## 137.8333 136.8333
DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.
pch Plot
xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
y = rep(5:1, each = 6, length.out = 26),
pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
geom_point(color = "darkred", fill = "darkblue", size = 5) +
geom_text(aes(label = pch), nudge_x = -0.25) +
scale_shape_manual(values = xx$pch) +
scale_x_continuous(breaks = 6:1) +
scale_y_continuous(breaks = 6:1) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Plot symbols in R (pch)",
subtitle = "color = \"darkred\", fill = \"darkblue\"",
x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")R Markdown
Tutorials on how to create an R markdown document like this one can be found here:
- https://rmarkdown.rstudio.com/articles_intro.html
- https://rmarkdown.rstudio.com/lesson-1.html
- https://alexd106.github.io/intro2R/Rmarkdown_intro.html